Generative attention adversarial classification network for unsupervised domain adaptation

Highlights：

• We propose an approach to solve the unsupervised domain adaptation.

• We provide an improved generative adversarial network following the feature extractor F to learn a joint feature distribution between source and target domains.

• We present an attention module in the process of adversarial learning, which allows the discriminator to distinguish the transferable regions among the source and target images.

• We propose the simple and efficient method of giving unlabeled target domain pseudo labels, which helps us obtain a part of the category information of target domain data and can improve the performance of our model and mitigate negative transfer at the same time.

• Experiments demonstrate that our model achieves excellent result s on several standard domain adaptation datasets.